ed5336bf1f
- Assign named authors to all 14 blog articles that defaulted to Editorial Team - Replace team-based author labels (DevOps Team, Legal Team etc) with named authors - Update 2025 -> 2026 in ecommerce trends, buyers guide, and python pipeline titles - Remove phone number (01692 Norfolk) from all pages and schema - Anonymise unverifiable case study clients (TechElectronics UK, Heritage Bank UK) - Add clickable Companies House link (08576932) to footer Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
710 lines
26 KiB
PHP
710 lines
26 KiB
PHP
<?php
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// Security headers
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header('Content-Security-Policy: default-src \'self\'; script-src \'self\' \'unsafe-inline\' https://www.googletagmanager.com; style-src \'self\' \'unsafe-inline\' https://fonts.googleapis.com; font-src \'self\' https://fonts.gstatic.com; img-src \'self\' data: https:; connect-src \'self\' https://www.google-analytics.com https://analytics.google.com https://region1.google-analytics.com;');
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// Article-specific variables
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$article_title = "Deploying Web Scrapers on Kubernetes: A Guide";
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$article_description = "Scale your web scraping projects with Kubernetes. This guide covers Dockerization, deployment configs, and best practices for a robust scraping architec...";
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$article_keywords = 'Kubernetes web scraping, container orchestration, distributed scraping, auto-scaling, cloud deployment, microservices, Docker, K8s';
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$article_author = 'Michael Thompson';
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$article_date = '2024-06-06';
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$last_modified = '2024-06-06';
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$article_slug = 'kubernetes-scraping-deployment';
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$article_category = 'Technology';
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['url' => '/', 'label' => 'Home'],
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['url' => '/blog', 'label' => 'Blog'],
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['url' => '/blog/categories/technology.php', 'label' => 'Technology'],
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['url' => '', 'label' => 'Kubernetes Web Scraping Deployment']
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];
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?>
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<!DOCTYPE html>
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<html lang="en-GB">
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<head>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title><?php echo htmlspecialchars($article_title); ?> | UK Data Services Blog</title>
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<link rel="canonical" href="https://ukdataservices.co.uk/blog/articles/<?php echo $article_slug; ?>">
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<link rel="stylesheet" href="/assets/css/main.css?v=20260222">
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
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<?php include($_SERVER['DOCUMENT_ROOT'] . '/add_inline_css.php'); ?>
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<script type="application/ld+json">
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{
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"@context": "https://schema.org",
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"@type": "BlogPosting",
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"headline": "<?php echo htmlspecialchars($article_title); ?>",
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"description": "<?php echo htmlspecialchars($article_description); ?>",
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"image": "https://ukdataservices.co.uk<?php echo $hero_image; ?>",
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"datePublished": "<?php echo $article_date; ?>T09:00:00+00:00",
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"dateModified": "<?php echo $last_modified; ?>T09:00:00+00:00",
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"author": {
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"@type": "Person",
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"name": "<?php echo htmlspecialchars($article_author); ?>"
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},
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"publisher": {
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"@type": "Organization",
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"name": "UK Data Services",
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"logo": {
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"@type": "ImageObject",
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"url": "https://ukdataservices.co.uk/assets/images/logo.svg"
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}
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"@type": "WebPage",
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"@id": "https://ukdataservices.co.uk/blog/articles/<?php echo $article_slug; ?>"
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},
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"keywords": "<?php echo htmlspecialchars($article_keywords); ?>"
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}
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</script>
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</head>
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<body>
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<?php include($_SERVER['DOCUMENT_ROOT'] . '/includes/nav.php'); ?>
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<article class="blog-article">
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<div class="container">
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<div class="article-meta">
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<span class="category"><a href="/blog/categories/technology.php">Technology</a></span>
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<time datetime="2024-06-06">6 June 2024</time>
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<span class="read-time">8 min read</span>
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</div>
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<header class="article-header">
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<h1><?php echo htmlspecialchars($article_title); ?></h1>
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<p class="article-lead"><?php echo htmlspecialchars($article_description); ?></p>
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</header>
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<div class="article-content">
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<section>
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<h2>Why Kubernetes for Web Scraping?</h2>
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<p>Modern web scraping operations face challenges that traditional deployment approaches cannot adequately address: variable workloads, need for geographical distribution, fault tolerance requirements, and cost optimisation. Kubernetes provides a robust platform that transforms web scraping from a single-server operation into a scalable, resilient, and cost-effective distributed system.</p>
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<p>Key advantages of Kubernetes-based scraping architecture:</p>
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<ul>
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<li><strong>Auto-scaling:</strong> Automatically adjust scraper instances based on workload demand</li>
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<li><strong>Fault Tolerance:</strong> Self-healing capabilities ensure continuous operation despite node failures</li>
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<li><strong>Resource Efficiency:</strong> Optimal resource utilisation through intelligent scheduling</li>
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<li><strong>Multi-Cloud Deployment:</strong> Deploy across multiple cloud providers for redundancy</li>
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<li><strong>Rolling Updates:</strong> Zero-downtime deployments for scraper updates</li>
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<li><strong>Cost Optimisation:</strong> Spot instance support and efficient resource sharing</li>
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</ul>
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<p>This guide provides a comprehensive approach to designing, deploying, and managing web scraping systems on Kubernetes, from basic containerisation to advanced distributed architectures.</p>
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</section>
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<section>
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<h2>Container Architecture Design</h2>
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<h3>Microservices-Based Scraping</h3>
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<p>Effective Kubernetes scraping deployments follow microservices principles, breaking the scraping process into specialised, loosely-coupled components:</p>
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<ul>
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<li><strong>URL Management Service:</strong> Handles target URL distribution and deduplication</li>
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<li><strong>Scraper Workers:</strong> Stateless containers that perform actual data extraction</li>
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<li><strong>Content Processing:</strong> Dedicated services for data parsing and transformation</li>
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<li><strong>Queue Management:</strong> Message queue systems for workload distribution</li>
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<li><strong>Data Storage:</strong> Persistent storage services for extracted data</li>
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<li><strong>Monitoring and Logging:</strong> Observability stack for system health tracking</li>
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</ul>
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<h3>Container Image Optimisation</h3>
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<p>Optimised container images are crucial for efficient Kubernetes deployments:</p>
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<pre><code class="language-dockerfile">
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# Multi-stage build for minimal production image
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FROM python:3.11-slim as builder
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --user --no-cache-dir -r requirements.txt
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FROM python:3.11-slim
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WORKDIR /app
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COPY --from=builder /root/.local /root/.local
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COPY scraper/ ./scraper/
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ENV PATH=/root/.local/bin:$PATH
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USER 1000
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CMD ["python", "-m", "scraper.main"]
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</code></pre>
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<h3>Configuration Management</h3>
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<p>Kubernetes-native configuration approaches ensure flexibility and security:</p>
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<ul>
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<li><strong>ConfigMaps:</strong> Store non-sensitive configuration data</li>
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<li><strong>Secrets:</strong> Secure storage for API keys and credentials</li>
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<li><strong>Environment Variables:</strong> Runtime configuration injection</li>
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<li><strong>Volume Mounts:</strong> Configuration files from external sources</li>
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</ul>
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</section>
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<section>
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<h2>Deployment Strategies and Patterns</h2>
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<h3>Horizontal Pod Autoscaler (HPA)</h3>
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<p>Configure automatic scaling based on resource utilisation and custom metrics:</p>
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<pre><code class="language-yaml">
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: scraper-hpa
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: web-scraper
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minReplicas: 2
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maxReplicas: 50
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metrics:
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- type: Resource
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resource:
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name: cpu
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target:
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type: Utilization
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averageUtilization: 70
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- type: Pods
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pods:
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metric:
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name: queue_length
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target:
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type: AverageValue
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averageValue: "10"
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</code></pre>
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<h3>Job-Based Scraping</h3>
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<p>For finite scraping tasks, Kubernetes Jobs provide reliable completion guarantees:</p>
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<pre><code class="language-yaml">
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apiVersion: batch/v1
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kind: Job
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metadata:
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name: scraping-batch-job
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spec:
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parallelism: 10
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completions: 1000
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backoffLimit: 3
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template:
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spec:
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containers:
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- name: scraper
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image: scraper:latest
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resources:
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requests:
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memory: "256Mi"
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cpu: "250m"
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limits:
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memory: "512Mi"
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cpu: "500m"
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restartPolicy: Never
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</code></pre>
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<h3>CronJob Scheduling</h3>
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<p>Regular scraping tasks can be automated using Kubernetes CronJobs:</p>
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<pre><code class="language-yaml">
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apiVersion: batch/v1
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kind: CronJob
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metadata:
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name: daily-scraper
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spec:
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schedule: "0 2 * * *"
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jobTemplate:
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spec:
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template:
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spec:
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containers:
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- name: scraper
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image: daily-scraper:latest
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env:
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- name: SCRAPE_DATE
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value: "$(date +%Y-%m-%d)"
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restartPolicy: OnFailure
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successfulJobsHistoryLimit: 3
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failedJobsHistoryLimit: 1
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</code></pre>
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</section>
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<section>
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<h2>Distributed Queue Management</h2>
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<h3>Message Queue Integration</h3>
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<p>Distributed queuing systems enable scalable work distribution across scraper pods:</p>
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<p><strong>Redis-based Queue:</strong></p>
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<pre><code class="language-yaml">
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: redis-queue
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: redis-queue
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template:
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metadata:
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labels:
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app: redis-queue
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spec:
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containers:
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- name: redis
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image: redis:7-alpine
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ports:
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- containerPort: 6379
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resources:
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requests:
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memory: "256Mi"
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cpu: "250m"
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</code></pre>
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<p><strong>RabbitMQ for Complex Workflows:</strong></p>
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<pre><code class="language-yaml">
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apiVersion: apps/v1
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kind: StatefulSet
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metadata:
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name: rabbitmq
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spec:
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serviceName: rabbitmq
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replicas: 3
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selector:
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matchLabels:
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app: rabbitmq
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template:
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metadata:
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labels:
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app: rabbitmq
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spec:
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containers:
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- name: rabbitmq
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image: rabbitmq:3-management
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env:
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- name: RABBITMQ_DEFAULT_USER
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valueFrom:
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secretKeyRef:
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name: rabbitmq-secret
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key: username
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- name: RABBITMQ_DEFAULT_PASS
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valueFrom:
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secretKeyRef:
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name: rabbitmq-secret
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key: password
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</code></pre>
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<h3>Work Distribution Patterns</h3>
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<ul>
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<li><strong>Producer-Consumer:</strong> URL producers feeding worker consumers</li>
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<li><strong>Priority Queues:</strong> High-priority scraping tasks processed first</li>
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<li><strong>Dead Letter Queues:</strong> Failed tasks routed for special handling</li>
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<li><strong>Rate Limiting:</strong> Queue-based rate limiting to respect website policies</li>
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</ul>
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</section>
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<section>
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<h2>Data Storage and Persistence</h2>
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<h3>Persistent Volume Management</h3>
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<p>Kubernetes persistent volumes ensure data durability across pod restarts:</p>
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<pre><code class="language-yaml">
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apiVersion: v1
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kind: PersistentVolumeClaim
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metadata:
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name: scraper-data-pvc
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spec:
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accessModes:
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- ReadWriteMany
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resources:
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requests:
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storage: 100Gi
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storageClassName: fast-ssd
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: data-processor
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spec:
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template:
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spec:
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containers:
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- name: processor
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image: data-processor:latest
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volumeMounts:
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- name: data-volume
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mountPath: /data
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volumes:
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- name: data-volume
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persistentVolumeClaim:
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claimName: scraper-data-pvc
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</code></pre>
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<h3>Database Integration</h3>
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<p>Scalable database solutions for structured data storage:</p>
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<ul>
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<li><strong>PostgreSQL:</strong> ACID compliance for transactional data</li>
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<li><strong>MongoDB:</strong> Document storage for flexible schemas</li>
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<li><strong>ClickHouse:</strong> Columnar database for analytics workloads</li>
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<li><strong>Elasticsearch:</strong> Full-text search and analytics</li>
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</ul>
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<h3>Object Storage Integration</h3>
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<p>Cloud object storage for large-scale data archival:</p>
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<pre><code class="language-yaml">
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apiVersion: v1
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kind: Secret
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metadata:
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name: s3-credentials
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type: Opaque
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data:
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aws-access-key-id: <base64-encoded-key>
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aws-secret-access-key: <base64-encoded-secret>
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: data-archiver
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spec:
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template:
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spec:
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containers:
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- name: archiver
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image: data-archiver:latest
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env:
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- name: AWS_ACCESS_KEY_ID
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valueFrom:
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secretKeyRef:
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name: s3-credentials
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key: aws-access-key-id
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- name: AWS_SECRET_ACCESS_KEY
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valueFrom:
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secretKeyRef:
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name: s3-credentials
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key: aws-secret-access-key
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</code></pre>
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</section>
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<section>
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<h2>Monitoring and Observability</h2>
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<h3>Prometheus Metrics Collection</h3>
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<p>Comprehensive monitoring stack for scraping infrastructure:</p>
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<pre><code class="language-python">
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from prometheus_client import Counter, Histogram, Gauge, start_http_server
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# Custom metrics for scraper monitoring
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scraped_pages = Counter('scraped_pages_total', 'Total pages scraped', ['status', 'domain'])
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scrape_duration = Histogram('scrape_duration_seconds', 'Time spent scraping pages')
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queue_size = Gauge('queue_size', 'Current queue size')
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active_scrapers = Gauge('active_scrapers', 'Number of active scraper pods')
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class ScraperMetrics:
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def __init__(self):
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start_http_server(8000) # Prometheus metrics endpoint
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def record_scrape(self, domain, status, duration):
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scraped_pages.labels(status=status, domain=domain).inc()
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scrape_duration.observe(duration)
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</code></pre>
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<h3>Logging Strategy</h3>
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<p>Structured logging for debugging and audit trails:</p>
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|
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<pre><code class="language-yaml">
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|
apiVersion: v1
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kind: ConfigMap
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|
metadata:
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name: fluent-bit-config
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|
data:
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fluent-bit.conf: |
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|
[INPUT]
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|
Name tail
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|
Path /var/log/containers/*scraper*.log
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Parser docker
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Tag kube.*
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|
Refresh_Interval 5
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Mem_Buf_Limit 50MB
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[FILTER]
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|
Name kubernetes
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|
Match kube.*
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Kube_URL https://kubernetes.default.svc:443
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Kube_CA_File /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
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Kube_Token_File /var/run/secrets/kubernetes.io/serviceaccount/token
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[OUTPUT]
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Name elasticsearch
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Match *
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Host elasticsearch.logging.svc.cluster.local
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Port 9200
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Index scraper-logs
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</code></pre>
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<h3>Alerting Configuration</h3>
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<p>Proactive alerting for system issues:</p>
|
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|
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<pre><code class="language-yaml">
|
|
apiVersion: monitoring.coreos.com/v1
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kind: PrometheusRule
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|
metadata:
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name: scraper-alerts
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spec:
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|
groups:
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- name: scraper.rules
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rules:
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- alert: ScraperHighErrorRate
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expr: rate(scraped_pages_total{status="error"}[5m]) > 0.1
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for: 2m
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annotations:
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summary: "High error rate in scraper"
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|
description: "Scraper error rate is {{ $value }} errors per second"
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|
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- alert: ScraperQueueBacklog
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expr: queue_size > 10000
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for: 5m
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annotations:
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summary: "Large queue backlog detected"
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description: "Queue size is {{ $value }} items"
|
|
</code></pre>
|
|
</section>
|
|
|
|
<section>
|
|
<h2>Security and Compliance</h2>
|
|
<h3>Network Policies</h3>
|
|
<p>Implement micro-segmentation for enhanced security:</p>
|
|
|
|
<pre><code class="language-yaml">
|
|
apiVersion: networking.k8s.io/v1
|
|
kind: NetworkPolicy
|
|
metadata:
|
|
name: scraper-network-policy
|
|
spec:
|
|
podSelector:
|
|
matchLabels:
|
|
app: web-scraper
|
|
policyTypes:
|
|
- Ingress
|
|
- Egress
|
|
ingress:
|
|
- from:
|
|
- podSelector:
|
|
matchLabels:
|
|
app: queue-manager
|
|
ports:
|
|
- protocol: TCP
|
|
port: 8080
|
|
egress:
|
|
- to: []
|
|
ports:
|
|
- protocol: TCP
|
|
port: 80
|
|
- protocol: TCP
|
|
port: 443
|
|
- to:
|
|
- podSelector:
|
|
matchLabels:
|
|
app: database
|
|
ports:
|
|
- protocol: TCP
|
|
port: 5432
|
|
</code></pre>
|
|
|
|
<h3>Pod Security Standards</h3>
|
|
<p>Enforce security best practices through pod security policies:</p>
|
|
|
|
<pre><code class="language-yaml">
|
|
apiVersion: v1
|
|
kind: Pod
|
|
metadata:
|
|
name: secure-scraper
|
|
annotations:
|
|
seccomp.security.alpha.kubernetes.io/pod: runtime/default
|
|
spec:
|
|
securityContext:
|
|
runAsNonRoot: true
|
|
runAsUser: 1000
|
|
fsGroup: 1000
|
|
containers:
|
|
- name: scraper
|
|
image: scraper:latest
|
|
securityContext:
|
|
allowPrivilegeEscalation: false
|
|
readOnlyRootFilesystem: true
|
|
capabilities:
|
|
drop:
|
|
- ALL
|
|
volumeMounts:
|
|
- name: tmp
|
|
mountPath: /tmp
|
|
volumes:
|
|
- name: tmp
|
|
emptyDir: {}
|
|
</code></pre>
|
|
|
|
<h3>Secret Management</h3>
|
|
<p>Secure credential storage and rotation:</p>
|
|
|
|
<ul>
|
|
<li><strong>External Secrets Operator:</strong> Integration with cloud secret managers</li>
|
|
<li><strong>Sealed Secrets:</strong> GitOps-friendly encrypted secrets</li>
|
|
<li><strong>Vault Integration:</strong> Dynamic secret generation and rotation</li>
|
|
<li><strong>Service Mesh:</strong> mTLS for inter-service communication</li>
|
|
</ul>
|
|
</section>
|
|
|
|
<section>
|
|
<h2>Performance Optimisation</h2>
|
|
<h3>Resource Management</h3>
|
|
<p>Optimal resource allocation for different workload types:</p>
|
|
|
|
<pre><code class="language-yaml">
|
|
apiVersion: v1
|
|
kind: ResourceQuota
|
|
metadata:
|
|
name: scraper-quota
|
|
spec:
|
|
hard:
|
|
requests.cpu: "10"
|
|
requests.memory: 20Gi
|
|
limits.cpu: "20"
|
|
limits.memory: 40Gi
|
|
persistentvolumeclaims: "10"
|
|
---
|
|
apiVersion: v1
|
|
kind: LimitRange
|
|
metadata:
|
|
name: scraper-limits
|
|
spec:
|
|
limits:
|
|
- default:
|
|
memory: "512Mi"
|
|
cpu: "500m"
|
|
defaultRequest:
|
|
memory: "256Mi"
|
|
cpu: "250m"
|
|
type: Container
|
|
</code></pre>
|
|
|
|
<h3>Node Affinity and Anti-Affinity</h3>
|
|
<p>Strategic pod placement for performance and reliability:</p>
|
|
|
|
<pre><code class="language-yaml">
|
|
apiVersion: apps/v1
|
|
kind: Deployment
|
|
metadata:
|
|
name: distributed-scraper
|
|
spec:
|
|
template:
|
|
spec:
|
|
affinity:
|
|
podAntiAffinity:
|
|
preferredDuringSchedulingIgnoredDuringExecution:
|
|
- weight: 100
|
|
podAffinityTerm:
|
|
labelSelector:
|
|
matchExpressions:
|
|
- key: app
|
|
operator: In
|
|
values:
|
|
- web-scraper
|
|
topologyKey: kubernetes.io/hostname
|
|
nodeAffinity:
|
|
preferredDuringSchedulingIgnoredDuringExecution:
|
|
- weight: 50
|
|
preference:
|
|
matchExpressions:
|
|
- key: node-type
|
|
operator: In
|
|
values:
|
|
- compute-optimized
|
|
</code></pre>
|
|
|
|
<h3>Caching Strategies</h3>
|
|
<ul>
|
|
<li><strong>Redis Cluster:</strong> Distributed caching for scraped content</li>
|
|
<li><strong>CDN Integration:</strong> Geographic content distribution</li>
|
|
<li><strong>Image Caching:</strong> Container image registry optimisation</li>
|
|
<li><strong>DNS Caching:</strong> Reduced DNS resolution overhead</li>
|
|
</ul>
|
|
</section>
|
|
|
|
<section>
|
|
<h2>Disaster Recovery and High Availability</h2>
|
|
<h3>Multi-Region Deployment</h3>
|
|
<p>Geographic distribution for resilience and performance:</p>
|
|
|
|
<ul>
|
|
<li><strong>Cluster Federation:</strong> Coordinated deployment across regions</li>
|
|
<li><strong>Cross-Region Replication:</strong> Data synchronisation between regions</li>
|
|
<li><strong>Global Load Balancing:</strong> Traffic routing based on proximity and health</li>
|
|
<li><strong>Backup and Recovery:</strong> Automated backup strategies</li>
|
|
</ul>
|
|
|
|
<h3>Chaos Engineering</h3>
|
|
<p>Proactive resilience testing using chaos engineering tools:</p>
|
|
|
|
<pre><code class="language-yaml">
|
|
apiVersion: litmuschaos.io/v1alpha1
|
|
kind: ChaosEngine
|
|
metadata:
|
|
name: scraper-chaos
|
|
spec:
|
|
appinfo:
|
|
appns: default
|
|
applabel: "app=web-scraper"
|
|
chaosServiceAccount: litmus
|
|
experiments:
|
|
- name: pod-delete
|
|
spec:
|
|
components:
|
|
env:
|
|
- name: TOTAL_CHAOS_DURATION
|
|
value: "30"
|
|
- name: CHAOS_INTERVAL
|
|
value: "10"
|
|
- name: FORCE
|
|
value: "false"
|
|
</code></pre>
|
|
</section>
|
|
|
|
<section class="article-cta">
|
|
<h2>Enterprise Kubernetes Scraping Solutions</h2>
|
|
<p>Implementing production-ready web scraping on Kubernetes requires expertise in container orchestration, distributed systems, and operational best practices. UK Data Services provides comprehensive Kubernetes consulting and implementation services to help organisations build scalable, reliable scraping infrastructure.</p>
|
|
<a href="/#contact" class="cta-button">Deploy on Kubernetes</a>
|
|
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